Fast, robust anatomical sketch recognition

Abstract:

Sketches are often used in medicine. Sketchin
g provides an effectiv
e means of illustration.
Physicians commonly use sketches when taking notes in patient records and to help convey
diagnoses and treatments to patients. In medical learning, students frequently use sketches to
help them think through clinical problems in individual and group problem solving. So the
ability to recognize those sketches
could bring benefit in many medical applications such as
automated patient records and education software in medicine. With the support of tablet PC
or pen-based devices, these applications become more feasible. In this thesis, I develop a new
approach to recognizing anatomical sketches. The approach consists of two steps. The first
step is for calculating the similarity between the
sketch and a set of templates with the notice
that the sketches are really different in the amount of the detail and whereas the internal details
of medical sketches vary within a given catego
ry, the outline is relativ
ely stable. The second
step uses Support Vector Machine to classify the sketch. The results of the approaches are
evaluated over a number of sketches drawn by medical students. To measure the effectiveness
of the approaches, the accuracies of the recognition between the system and medical students
are compared.